Complex Wavefront Shaping through a Multi-Core Fiber

Abstract

Wavefront shaping through a multi-core fiber (MCF) is turning into an attractive method for endoscopic imaging and optical cell-manipulation on a chip. However, the discrete distribution and the low number of cores induce pixelated phase modulation, becoming an obstacle for delivering complex light field distributions through MCFs. We demonstrate a novel phase retrieval algorithm named Core–Gerchberg–Saxton (Core-GS) employing the captured core distribution map to retrieve tailored modulation hologram for the targeted intensity distribution at the distal far-field. Complex light fields are reconstructed through MCFs with high fidelity up to 96.2%. Closed-loop control with experimental feedback denotes the capability of the Core-GS algorithm for precise intensity manipulation of the reconstructed light field. Core-GS provides a robust way for wavefront shaping through MCFs; it facilitates the MCF becoming a vital waveguide in endoscopic and lab-on-a-chip applications.

Real-time complex light field generation through a multi-core fiber with deep learning

“…Then the MCF can act as a phased array to generate arbitrary light field distribution. Typically, to control the wavefront through the MCF, a tailored CGH generated by the Core-GS algorithm 27 is loaded to the SLM additionally to the conjugated phase. Although the Core-GS algorithm can achieve good quality compared to the raw GS algorithm, the long iteration time still limits the applications.…”

“…3 a. Different from the Core-GS 27 , CoreNet provides optimal recovery of the target image without any blemishes. It can be noticed in Fig.…”

“…However, the random and discrete distribution of fiber cores leads to a strong spatial sampling of the CGH when coupling into the MCF, and the number of fiber cores is much less than the pixel number of the phase-only SLM, inducing strong aliasing. This results in unrecognizable reconstructed light fields 27 , proving the GS algorithm cannot be directly implemented in hologram generation for randomly distributed phased arrays. Therefore, we previously proposed a tailored phase retrieval algorithm for holographic control of complex light field through the MCF named Core-GS 27 , enabling complex wavefront shaping through the MCF with high fidelity.…”

“…This results in unrecognizable reconstructed light fields 27 , proving the GS algorithm cannot be directly implemented in hologram generation for randomly distributed phased arrays. Therefore, we previously proposed a tailored phase retrieval algorithm for holographic control of complex light field through the MCF named Core-GS 27 , enabling complex wavefront shaping through the MCF with high fidelity. This is implemented in lab-on-a-chip optical manipulation of biological cells 28 and can facilitate applications in holographic optogenetic stimulation 29 , micro-materials processing in hard-to-reach areas 30 , structured light generation for MCF amplifier 31 , and high-dimensional optical and quantum communication 32 .…”

“…Therefore, there is a great demand in biomedicine for real-time generation of tailored CGHs for MCF. Recently, deep neural networks have been used for reducing the computation time of CGHs 35 – 39 , however, all of these existing networks are designed for regularly distributed pixels and induce significant distortion when applied to discrete or randomly distributed phased-arrays 23 , 27 . Therefore, an approach that can generate tailored CGHs for MCFs or random phased arrays in real-time with high fidelity is not yet available.…”